Both genes and environmental exposures during fetal development likely contribute to autism. We are interested in identifying risks that are preventable. Valid exposure assessment methods in population-based epidemiologic studies are a key factor for reaching this goal. Here, we propose to assess whether autism risk factors can be identified using metabolomic biomarkers of exposure in stored maternal serum samples from mid-pregnancy and compare biomarker exposure patterns with modelled air pollution and pesticide exposures in a largely immigrant population of Hispanic mothers, a vulnerable and understudied population not previously examined in California, living in the Central Valley known for its farming related pesticide exposures and high particulate air pollution from diesel, wood-burning and wildfires. We have almost two decades of experience building exposure models for pesticides and air pollution in California 1-3 and studying fetal development 4-8 and more recently expanded our research to include autism9. California has unique resources: 1) monitoring data to model common pesticides and air pollutants at the population-level; 2) stored prenatal serum samples from the State's Prenatal Expanded Alphafetoprotein Screening Program (XRAF) for the Central Valley population since the year 2000; and 3) a statewide system of regional centers servicing person with autism and other developmental disabilities (CA Department of Developmental Services (DDS)). We propose to employ these resources in a new and innovative manner combining our expertise in environmental modeling of exposures with the resources in Dr. Jones' laboratory at Emory University to identify metabolomics exposure markers and potential disease biomarkers for autism. Metabolomics analyses will be performed in a targeted as well as untargeted manner to identify and quantify a number of prenatal exposures and their metabolites in mid-pregnancy serum from 200 case and 200 control pregnancies in Central California. Jones has developed high-resolution metabolomics that uses mass spectrometry and advanced data extraction algorithms to quantify up to 20,000 chemicals in small biologic extracts 10-12. This novel approach will be used to identify exposure and disease 'signatures' from metabolites in human metabolic pathways and environmental exposures. This will help us test existing and generate new hypotheses about maternal exposures (e.g. PAHs, pesticides, endocrine disruptors) and metabolic states that affect neurodevelopment in the offspring; focusing on air pollution and neurotoxic pesticides. We expect this application to provide necessary evidence for larger scale explorations of autism and exposure biomarkers; encourage environmental policies and provide opportunities for population based prevention of autism.